Recent advances in AI-driven cancer nanotherapy

Cancer, characterized by its high incidence and mortality rates, has emerged as a major global public health challenge. However, cancer treatment has long been hampered by significant side effects, difficulties in optimizing therapeutic strategies, considerable inter-patient variability in treatment response, and a reliance on empirical approaches. The development of nanomaterials has promoted the emergence of nanocarrier-assisted chemotherapy, photothermal therapy and photodynamic therapy. Though these modalities improved the spatiotemporal control of cancer therapy, the accurate evaluation of nanodrugs on personalized safety and therapeutic efficacy is challenging. In recent years, the integration of artificial intelligence (AI) technologies into cancer therapeutics, particularly the convergence of core techniques such as machine learning and deep learning, has revolutionized the development, optimization, and real-time monitoring of cancer treatment regimens. This review summarizes the cutting-edge applications of AI technologies in cancer nanotherapy, including biosafety prediction in chemotherapy, parameter optimization and treatment monitoring in photothermal therapy, as well as dosage determination and efficacy evaluation in photodynamic therapy. It highlights the pivotal role of AI in advancing cancer treatment toward precision and personalization, while also discussing existing challenges and outlining promising future directions.